General Collective Intelligence and the Transition to Collective Super-Intelligence

Abstract

For at least a decade a general collective intelligence factor measuring the general problem-solving ability (intelligence) of groups has been hypothesized by many to exist. General Collective Intelligence or GCI has recently been defined as a platform that combines individuals into a single collective cognition that may have vastly greater intelligence than any individual, and avastly greater general collective intelligence factor than is innate to any group. A novel Human-Centric Functional Modeling approach has been used define both a model for this collective cognition, as well as a method for assessing the intelligence of a system of individual or collective cognition, in order to quantify this potential increase in intelligence as exponential. From the functional modeling perspective, the transition from animal intelligence to a human intelligence, is a well-defined phase change. The transition from human intelligence to GCI, the transition from GCI to second order GCI, and so forth to Nth order GCI, are also potential phase changes. The functional modeling approach clarifies the fundamentally different nature of the general problem-solving ability provided by GCI as opposed to the narrow problem solving ability of tools such as computation (including artificial intelligence) that can be applied to any general problem. This comparison suggests that entire categories of problems cannot reliably be solved without this phase change to GCI. However, this same functional modeling approach suggests that GCI is too big and complex, and requires too great a degree of flexibility, to be reliably implemented in a top-down way without GCI, so instead must be evolved in a self-assembling process that relies on GCI itself